Evidence map›Paper›PMID 35819498›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2022

Assessing dynamic metabolic heterogeneity in non-small cell lung cancer patients via ultra-high sensitivity total-body [

DaQuan Wang, Xu Zhang, Hui Liu, Bo Qiu, SongRan Liu, ChaoJie Zheng, Jia Fu, YiWen Mo, NaiBin Chen, Rui Zhou and 7 more

Abstract read
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In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
5.5field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 39 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Correlations between Ki67 expression and uptake ofCancer imaging : the official publication of the International Cancer Imaging Society · 2025
    Article
  5. Abbreviated dynamic total-body [Quantitative imaging in medicine and surgery · 2025
    Article
  6. Article
  7. Review
  8. Article
  9. Self-supervised neural network for Patlak-based parametric imaging in dynamic [European journal of nuclear medicine and molecular imaging · 2025
    Article
  10. Article
  11. Article
  12. Repeated dynamic [European journal of nuclear medicine and molecular imaging · 2024
    Observational
  13. Review
  14. Application of Dynamic [Molecular imaging and biology · 2024
    Article
  15. Review
  16. Article
  17. Characteristics of whole-body dynamicQuantitative imaging in medicine and surgery · 2024
    Article
  18. Article
  19. Quantitation of dynamic total-body PET imaging: recent developments and future perspectives.European journal of nuclear medicine and molecular imaging · 2023
    Review
  20. Patlak-Ki derived from ultra-high sensitivity dynamic total body [European journal of nuclear medicine and molecular imaging · 2023
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors at 3 institutions in 1 country.

DaQuan Wang *Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
Xu Zhang *Department of Nuclear Medicine, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
Hui LiuUnited Imaging Healthcare, Shanghai, China.
Bo Qiu *Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
SongRan Liu *Department of Pathology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
ChaoJie ZhengUnited Imaging Healthcare, Shanghai, China.
Jia FuDepartment of Pathology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
YiWen MoDepartment of Nuclear Medicine, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
NaiBin ChenDepartment of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
Rui ZhouDepartment of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
Chu ChuDepartment of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
FangJie LiuDepartment of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
JinYu GuoDepartment of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
Yin ZhouSuZhou TongDiao Company, Suzhou, China.
Yun ZhouUnited Imaging Healthcare, Shanghai, China.
Wei FanDepartment of Nuclear Medicine, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China. fanwei@sysucc.org.cn.
Hui LiuDepartment of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China. liuhuisysucc@126.com.
Sun Yat-sen University Cancer Center · CNSun Yat-sen University · CNUnited Imaging Healthcare (China) · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study aimed to quantitatively assess [

methodsThe 60-min dynamic total-body [

resultsA total of 30 patients with stage IIIA-IV NSCLC were enrolled. Patients were divided into fast dynamic FDG metabolic group (F-DFM) and slow dynamic FDG metabolic group (S-DFM) by the unsupervised K-means classification of PTs. The F-DFM group showed significantly higher Patlak-Ki (P < 0.001) and SUV

conclusionThe dynamic total-body [

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsFluorodeoxyglucose F18HumansKi-67 AntigenLymph NodesPositron Emission Tomography Computed TomographyRetrospective StudiesTumor BurdenFluorodeoxyglucose F18Ki-67 Antigen[18F]FDGDynamic total-body PETLung cancerMetabolic heterogeneity

Identifiers

PMID35819498
OpenAlexW4285077719

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.